Objective: To present meta-analytic test accuracy estimates of levels of antitumour necrosis factor (anti-TNF) and antibodies to anti-TNF to predict loss of response or lack of regaining response in patients with anti-TNF managed Crohn's disease.
Methods: MEDLINE, Embase, the Cochrane Library and Science Citation Index were searched from inception to October/November 2014 to identify studies which reported 2×2 table data of the association between levels of anti-TNF or its antibodies and clinical status. Hierarchical/bivariate meta-analysis was undertaken with the user-written 'metandi' package of Harbord and Whiting using Stata V.11 software, for infliximab, adalimumab,anti-infliximab and anti-adalimumab levels as predictors of loss of response. Prevalence of Crohn's disease in included studies was meta-analysed using a random effects model in MetaAnalyst software to calculate positive and negative predictive values. The search was updated in January 2017.
Results: 31 studies were included in the review. Studies were heterogeneous with respect to the type of test used, criteria for establishing response and loss of response, population examined and results. Meta-analytic summary point estimates for sensitivity and specificity were 65.7% and 80.6% for infliximab trough levels and 56% and 79% for antibodies to infliximab, respectively. Pooled results for adalimumab trough levels and antibodies to adalimumab were similar. Pooled positive and negative predictive values ranged between 70% and 80% implying that between 20% and 30% of both positive and negative test results may be incorrect in predicting loss of response.
Conclusion: The available evidence suggests that these tests have modest predictive accuracy for clinical status; direct test accuracy comparisons in the same population are needed. More clinical trial evidence from test-treat studies is required before the clinical utility of the tests can be reliably evaluated.
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http://dx.doi.org/10.1136/bmjopen-2016-014581 | DOI Listing |
J Am Coll Cardiol
December 2024
Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, United Kingdom; Institute of Cardiovascular Science, University College London, London, United Kingdom.
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Laboratório Avançado de Saúde Pública, Instituto Gonçalo Moniz, Fundação Oswaldo Cruz (Fiocruz-BA), Salvador 40296-710, Bahia, Brazil.
Human T-cell leukemia virus type 1 (HTLV-1) is associated with an increased risk of tuberculosis (TB). This study aimed to evaluate the performance of the QuantiFERON-TB Gold (QFT) test for the diagnosis of (MTB) infection in HTLV-1-infected individuals. HTLV-1-infected participants were divided into four groups: HTLV-1-infected individuals with a history of tuberculosis (HTLV/TB), individuals with positive HTLV and tuberculin skin tests (HTLV/TST+) or negative TST (HTLV/TST-), and HTLV-1-negative individuals with positive TST results (HN/TST+).
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December 2024
Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK.
Climate change caused by greenhouse gas (GHG) emissions is an escalating global issue, with the transportation sector being a significant contributor, accounting for approximately a quarter of all energy-related GHG emissions. In the transportation sector, vehicle emissions testing is a key part of ensuring compliance with environmental regulations. The Vehicle Certification Agency (VCA) of the UK plays a pivotal role in certifying vehicles for compliance with emissions and safety standards.
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December 2024
Nokia Bell Labs, 1082 Budapest, Hungary.
Human action recognition using WiFi channel state information (CSI) has gained attention due to its non-intrusive nature and potential applications in healthcare, smart environments, and security. However, the reliability of methods developed for CSI-based action recognition is often contingent on the quality of the datasets and evaluation protocols used. In this paper, we uncovered a critical data leakage issue, which arises from improper data partitioning, in a widely used WiFi CSI benchmark dataset.
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December 2024
School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China.
Human pose estimation is an important research direction in the field of computer vision, which aims to accurately identify the position and posture of keypoints of the human body through images or videos. However, multi-person pose estimation yields false detection or missed detection in dense crowds, and it is still difficult to detect small targets. In this paper, we propose a Mamba-based human pose estimation.
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